This resource has been developed for educators of business students. The purpose of this resource is to assist educators in creating the opportunity for students to practice and develop their business report writing skills. It aims to do this in a collegial atmosphere which promotes social learning and allows for developmental feedback. It is argued that this most emulates the business setting and is most conducive to skills development..

Global investment in biomedical research has grown significantly over the last decades, reaching approximately a quarter of a trillion US dollars in 2010. However, not all of this investment is distributed evenly by gender. It follows, arguably, that scarce research resources may not be optimally invested (by either not supporting the best science or by failing to investigate topics that benefit women and men equitably). Women across the world tend to be significantly underrepresented in research both as researchers and research participants, receive less research funding, and appear less frequently than men as authors on research publications. There is also some evidence that women are relatively disadvantaged as the beneficiaries of research, in terms of its health, societal and economic impacts. Historical gender biases may have created a path dependency that means that the research system and the impacts of research are biased towards male researchers and male beneficiaries, making it inherently difficult (though not impossible) to eliminate gender bias. In this commentary, we - a group of scholars and practitioners from Africa, America, Asia and Europe - argue that gender-sensitive research impact assessment could become a force for good in moving science policy and practice towards gender equity. Research impact assessment is the multidisciplinary field of scientific inquiry that examines the research process to maximise scientific, societal and economic returns on investment in research. It encompasses many theoretical and methodological approaches that can be used to investigate gender bias and recommend actions for change to maximise research impact. We offer a set of recommendations to research funders, research institutions and research evaluators who conduct impact assessment on how to include and strengthen analysis of gender equity in research impact assessment and issue a global call for action.

This paper provides a citation network analysis of publications from the Academy of Management Journal, one of the key US-based journals in the field of Management. Our analysis covers all publications in the journal from 1958–2014. This represents the entire history of the journal until the arbitrary cut-off point of our study. The paper analyses the most published authors, most cited articles, most cited authors, top institutions, and the nationalities of authors that are most represented in the journal. 2304 articles containing 114,550 references were taken from the primary data source, the Web of Science™. An analysis of 114,550 citations was carried out using the Web of Science™ online analytics tool and Excel®. Gephi™, a data visualisation and manipulation software, was used to provide a visual representation of the citation networks. Results indicate that the most published authors within AMJ throughout the journal’s history are Ivancevich, Golembiewski and Hambrick. The three most cited authors within AMJ are Pfeffer, Porter and Thompson. The single most cited article is Pfeffer and Salancik’s 1978 article The external control of organizations: a resource dependence perspective. A keyword analysis revealed that the most important terms used in the journal’s history were ‘Performance’, ‘Organization’ and ‘Work’. Results from this paper extend our previous citation analyses of key journals in the discipline of Higher Education to a new discipline—the field of Management. The paper provides evidence of how visual analyses can help to represent the citation “geography” of a journal over time.

China’s growing outbound investment ambitions could be as transformative for today’s poor countries as inbound investment was for China. This will depend upon how recipient developing economies, in particular in Africa, utilise China’s investor interest for their own sustainable development.

INTRODUCTION: High levels of amyloid β (Aβ) are associated with cognitive decline in cognitively normal (CN) older adults. This study investigated the nature of cognitive decline in healthy individuals who did not progress to mild cognitive impairment or dementia. METHOD: Cognition was measured over 72 months and compared between low (Aβ-) and high (Aβ+) CN older adults (n = 335) who did not progress to mild cognitive impairment or dementia and who remained free of severe or uncontrolled systemic illness. RESULTS: Compared to the Aβ- group, the Aβ+ group showed no cognitive impairment at baseline but showed substantial decline in verbal learning, episodic memory, and attention over 72 months. DISCUSSION: Moderate cognitive decline, particularly for learning and memory, was associated with Aβ+ in CN older adults in the absence of clinical disease progression and uncontrolled or serious comorbid illness.

Accumulation of β-amyloid (Aβ) in the brain is associated with memory decline in healthy individuals as a prelude to Alzheimer's disease (AD). Genetic factors may moderate this decline. We examined the role of apolipoprotein E (ɛ4 carrier[ɛ4(+)], ɛ4 non-carrier[ɛ4(-)]) and brain-derived neurotrophic factor (BDNF(Val/Val), BDNF(Met)) in the extent to which they moderate Aβ-related memory decline. Healthy adults (n=333, Mage=70 years) enrolled in the Australian Imaging, Biomarkers and Lifestyle study underwent Aβ neuroimaging. Neuropsychological assessments were conducted at baseline, 18-, 36- and 54-month follow-ups. Aβ positron emission tomography neuroimaging was used to classify participants as Aβ(-) or Aβ(+). Relative to Aβ(-)ɛ4(-), Aβ(+)ɛ4(+) individuals showed significantly faster rates of cognitive decline over 54 months across all domains (d=0.40-1.22), while Aβ(+)ɛ4(-) individuals showed significantly faster decline only on verbal episodic memory (EM). There were no differences in rates of cognitive change between Aβ(-)ɛ4(-) and Aβ(-)ɛ4(+) groups. Among Aβ(+) individuals, ɛ4(+)/BDNF(Met) participants showed a significantly faster rate of decline on verbal and visual EM, and language over 54 months compared with ɛ4(-)/BDNF(Val/Val) participants (d=0.90-1.02). At least two genetic loci affect the rate of Aβ-related cognitive decline. Aβ(+)ɛ4(+)/BDNF(Met) individuals can expect to show clinically significant memory impairment after 3 years, whereas Aβ(+)ɛ4(+)/BDNF(Val/Val) individuals can expect a similar degree of impairment after 10 years. Little decline over 54 months was observed in the Aβ(-) and Aβ(+) ɛ4(-) groups, irrespective of BDNF status. These data raise important prognostic issues in managing preclinical AD, and should be considered in designing secondary preventative clinical trials.

Abstract Understanding age-related change in cognition and identification of pathological changes requires sensitive and valid measurement of cognitive performance across time. Technological advances, such as ambulatory assessment of cognition using smartphones, have enabled intensive longitudinal methods where data is collected with many measurements over time. Our research group has developed novel ambulatory assessments that provide reliable, sensitive, and ecologically valid measurement of cognition across multiple timescales; from momentary changes to change across years. This symposium will present a spectrum of approaches to analysis of intensive longitudinal data that can inform models of cognitive aging. All three presentations will draw on data from measurement burst studies that apply our ambulatory cognitive assessment methods in community-based samples (i.e., systematically recruited in the Bronx, New York). For each measurement burst, participants undergo assessment consisting of brief surveys and cognitive tests via smartphone, up to 7 times per day across 14 days. Oravecz et al. will discuss the application of a Bayesian multilevel implementation of the double exponential model to account for retest effects while quantifying change in peak cognitive performance across time. Kang et al., will demonstrate a growth curve modeling approach for assessing the effects of between-person variables (i.e., loneliness) on change in cognition across measurement bursts. Harrington et al., will demonstrate a model-based cluster analysis approach, leveraging ambulatory assessments of subjective and objective cognitive function to unpack latent groups as a function of age and loneliness. Measurement, Statistics, and Research Design Interest Group Sponsored Symposium.

Using data from a business school in a large research-intensive university in Australia, this study analyzes proposed teaching and learning changes with a focus on ‘closing the loop.’ Aspects of teaching and learning submitted by academic staff following assurance of learning (i.e. curriculum improvements) were analyzed using content analysis, spanning 382 program learning outcomes, 25 different degree programs, 117 subjects and 5828 pieces of individual student assessment (2009–2017). Analysis revealed six learning outcome themes, with ‘use, application and evaluation of relevant theories, methods concepts, ideas or models’ as most prominent. Suggested actions on each of the themes relate to various curricula changes, particularly (1) the move from teaching students ‘what to think’ to ‘how to think,’ (2) from developing fundamental to complex skills, and (3) providing more opportunities for feedback. Broader implications for teaching practice are discussed.